V7 cuts costs 78% while boosting accuracy with GPT-5.6 Luna

Today’s models can reason through complex tasks, but they don’t automatically understand the underlying business context of those tasks. Which fund report is current? How is the same entity named across three systems?

That context lives in documents, data rooms, spreadsheets, emails, and internal tools: scattered, unresolved, and invisible to agents. For teams in finance, insurance, and real estate, retrieval accuracy within workflows is non-negotiable.

After building a widely used computer vision accessibility app together, Rizzoli and Edwardsson started V7 ⁠ (opens in a new window) in 2018 to help companies teach AI systems how their businesses work. V7 Go is an agentic platform to build mission critical workflows, and organize buried context into memory that agents can query and act on.

V7 Go uses GPT‑5.6 Luna to extract information from millions of files and organize it in the Context Graph, which connects entities, relationships, and cited evidence, powering MCP search and repeatable workflows that can span hundreds of steps. For Workflows, V7 Go uses GPT‑5.6 Terra and Sol for reasoning and tool use across complex, multi-step instructions that take humans dozens of hours to complete. V7 is also starting to use GPT‑6 Astra on the most demanding Context Graph queries, including financial analysis across thousands of documents.

With context, models, and tools working together, V7 says agents complete 50–100 step workflows in minutes, reaching 99.9% accuracy, while maintaining an auditable trail of every decision made.

“To solve hard enterprise use cases across finance and insurance, AI needs to learn how your business operates just as well as it learned from the Internet.”

—Alberto Rizzoli, Co-Founder and CEO at V7

Giving agents the context to understand the whole business

The Context Graph solves a specific problem. Agents have to rediscover context on every request, leading to dozens of searches costing time and tokens, and often missing key information buried in relationships.

When data arrives, V7 Go connects to repositories such as SharePoint and Google Drive, scans them for entities, relationships, facts, attributes, and metrics, and populates a graph that’s an order of magnitude cheaper and faster to traverse than long-context approaches.

The Context Graph gives agents a structured, up-to-date record they can query directly. When a new file arrives, V7 Go identifies the companies, funds, people, or any entity in an ontology, then connects each fact to a new or existing record, and preserves cited evidence to the original source. If the graph does not contain enough information, V7 Go can still search the underlying documents with RAG.

V7 has also tested how much the structure of that context matters. On HERB, a benchmark for finding and connecting information spread across enterprise systems, V7’s retrieval-only system outperformed the official baseline by 69% and reduced hallucinations on un-answerable queries by 38%. V7 Go uses that source-linked context to keep complex workflows grounded in each company’s own information.

V7 Go uses that organized context in workflows such as private equity deal screening and insurance underwriting. In the demo below, a workflow extracts information from a deal document called a Confidential Information Memorandum (CIM) feeds key financials, deal terms, management details, and cites risk fields before V7 Go produces a screening note.

The Context Graph makes it so that a model can work with a firm’s history without relearning it each time. For long-running agents, V7 Go keeps recent exchanges in the model’s active context and stores older material in the graph to be retrieved when needed.

That shared context is already speeding up document-heavy work across V7’s customers:

- Asset managers can screen deals 21x faster than before, reducing a full-day process to just 15 minutes

- A financial services team cut review time from more than 100 hours to under 10, saving $12,000 in expert costs per task

- Insurance teams reduced errors in claims processing by 13.5% compared to a manual baseline, after granting their agents historical knowledge of all previous claims and existing policies

“With GPT-5.6 Terra, we have been able to remove many intermediate workflow stages that previously existed only to simplify the task for the model. It’s saved us days of delivery work and often gets things right on the first build of a workflow, thanks to a stronger model and access to more context.”

—Simon Edwardsson, Co-Founder and CTO at V7

Choosing OpenAI to keep complex workflows on track

Complex, multi-step workflows depend on a model reliably following lengthy instructions, running tools, interpreting results, and navigating a long series of steps. A single upstream error can lead to expensive consequences and broken trust in AI systems. V7 Go guides models across long horizon tasks spanning deterministic code, handovers to smaller models, file generation steps, and integrations, with an auditable trace of every run.

In AI-generated workflows, V7 Go maps each step to fast, medium, and smart tiers. GPT‑5.6 Luna handles structured extraction and other high-volume work, and GPT‑5.6 Terra or Sol power chat, the Go Agent path, and steps that require more reasoning or tool use.

V7 tests new models against a continuously maintained benchmark suite covering citation accuracy, extraction quality across hundreds of document types, answer correctness, instruction following, latency, cost, and real-world enterprise workflows. OpenAI outperforms most models on the behaviors V7 cares about most. OpenAI also approved and implemented V7’s capacity increase needs within hours, compared with weeks for other providers V7 uses.